{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10 minute ANTsPy tutorial\n",
    "\n",
    "ANTsPy is a Python wrapper for the ANTs neuroimage processing library, along with other pythonic tools that make it easy and fun to work with brain images directly in Python.\n",
    "\n",
    "This tutorial is available at https://github.com/ANTsX/ANTsPy/tree/master/tutorials.\n",
    "\n",
    "First, install ANTsPy. It can be installed from source (takes 30-60min) with the following code:\n",
    "\n",
    "```\n",
    "git clone https://github.com/ANTsX/ANTsPy.git\n",
    "cd ANTsPy\n",
    "python setup.py install\n",
    "```\n",
    "\n",
    "It can also be installed very quickly from pre-made wheels (latest release only available for Mac, but a previous release is available for Linux which should be OK): https://github.com/ANTsX/ANTsPy/releases . Download the .whl file then run the following:\n",
    "```\n",
    "pip install /path/to/file.whl\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ants\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image IO\n",
    "\n",
    "Reading and writing images is easy. ANTsPy has some included data which we will use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/stnava/.antspy/r16slice.jpg\n"
     ]
    }
   ],
   "source": [
    "fname1 = ants.get_ants_data('r16')\n",
    "fname2 = ants.get_ants_data('r64')\n",
    "print(fname1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANTsImage\n",
      "\t Pixel Type : float (float32)\n",
      "\t Components : 1\n",
      "\t Dimensions : (256, 256)\n",
      "\t Spacing    : (1.0, 1.0)\n",
      "\t Origin     : (0.0, 0.0)\n",
      "\t Direction  : [1. 0. 0. 1.]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "img1 = ants.image_read(fname1)\n",
    "img2 = ants.image_read(fname2)\n",
    "print(img1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can read also convert numpy arrays to ANTsImage types.. Here's an example of an fMRI image (an image with \"components\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANTsImage (RAI)\n",
      "\t Pixel Type : float (float32)\n",
      "\t Components : 10\n",
      "\t Dimensions : (70, 70, 70)\n",
      "\t Spacing    : (1.0, 1.0, 1.0)\n",
      "\t Origin     : (0.0, 0.0, 0.0)\n",
      "\t Direction  : [1. 0. 0. 0. 1. 0. 0. 0. 1.]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "arr_4d = np.random.randn(70,70,70,10).astype('float32')\n",
    "img_fmri = ants.from_numpy(arr_4d, has_components=True)\n",
    "print(img_fmri)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have an ANTsImage type, it basically acts as a numpy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.0, 1.0)\n",
      "(1.0, 1.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/ants/core/ants_image.py:476: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_array = this_array / other\n"
     ]
    }
   ],
   "source": [
    "# clone\n",
    "img = ants.image_read(fname1)\n",
    "img2 = img.clone()\n",
    "\n",
    "# convert to numpy\n",
    "img_arr = img.numpy()\n",
    "\n",
    "# create another image with same properties but different data\n",
    "img2 = img.new_image_like(img_arr*2)\n",
    "\n",
    "# save to file\n",
    "# img.to_file(...)\n",
    "\n",
    "# many useful things:\n",
    "img.median()\n",
    "img.std()\n",
    "img.argmin()\n",
    "img.argmax()\n",
    "img.flatten()\n",
    "img.nonzero()\n",
    "img.unique()\n",
    "\n",
    "# do any operations directly on ANTsImage types\n",
    "img3 = img2 - img\n",
    "img3 = img2 > img\n",
    "img3 = img2 / img\n",
    "img3 = img2 == img\n",
    "\n",
    "\n",
    "# change any physical properties\n",
    "img4 = img.clone()\n",
    "print(img4.spacing)\n",
    "img4.set_spacing((1,1))\n",
    "print(img4.spacing)\n",
    "\n",
    "# test if two images are allclose in values\n",
    "issame = ants.allclose(img,img2)\n",
    "\n",
    "# test if two images have same physical space\n",
    "issame_phys = ants.image_physical_space_consistency(img,img2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Segmentation\n",
    "\n",
    "This module includes Atropos segmentation, Joint Label Fusion, cortical thickness estimation, and prior-based segmentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Atropos segmentation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['segmentation', 'probabilityimages'])\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = ants.image_read(ants.get_ants_data('r16'))\n",
    "img = ants.resample_image(img, (64,64), 1, 0)\n",
    "mask = ants.get_mask(img)\n",
    "img_seg = ants.atropos(a=img, m='[0.2,1x1]', c='[2,0]', \n",
    "                       i='kmeans[3]', x=mask)\n",
    "print(img_seg.keys())\n",
    "ants.plot(img_seg['segmentation'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cortical thickness:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANTsImage\n",
      "\t Pixel Type : float (float32)\n",
      "\t Components : 1\n",
      "\t Dimensions : (256, 256)\n",
      "\t Spacing    : (1.0, 1.0)\n",
      "\t Origin     : (0.0, 0.0)\n",
      "\t Direction  : [1. 0. 0. 1.]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = ants.image_read( ants.get_ants_data('r16') ,2)\n",
    "mask = ants.get_mask( img ).threshold_image( 1, 2 )\n",
    "segs=ants.atropos( a = img, m = '[0.2,1x1]', c = '[2,0]',  i = 'kmeans[3]', x = mask )\n",
    "thickimg = ants.kelly_kapowski(s=segs['segmentation'], g=segs['probabilityimages'][1],\n",
    "                            w=segs['probabilityimages'][2], its=45, \n",
    "                            r=0.5, m=1)\n",
    "print(thickimg)\n",
    "img.plot(overlay=thickimg, overlay_cmap='jet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Registration\n",
    "\n",
    "This module includes the main ANTs registration interface, from which all registration algorithms can be run - along with various functions for evaluating registration algorithms or resampling/reorienting images or applying specific transformations to images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SyN registration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'warpedmovout': ANTsImage\n",
      "\t Pixel Type : float (float32)\n",
      "\t Components : 1\n",
      "\t Dimensions : (64, 64)\n",
      "\t Spacing    : (4.0476, 4.0476)\n",
      "\t Origin     : (0.0, 0.0)\n",
      "\t Direction  : [1. 0. 0. 1.]\n",
      ", 'warpedfixout': ANTsImage\n",
      "\t Pixel Type : float (float32)\n",
      "\t Components : 1\n",
      "\t Dimensions : (64, 64)\n",
      "\t Spacing    : (4.0476, 4.0476)\n",
      "\t Origin     : (0.0, 0.0)\n",
      "\t Direction  : [1. 0. 0. 1.]\n",
      ", 'fwdtransforms': ['/var/folders/6j/wrk7whwx57vgkytws9s8j42c0000gn/T/tmplr6t3kwx1Warp.nii.gz', '/var/folders/6j/wrk7whwx57vgkytws9s8j42c0000gn/T/tmplr6t3kwx0GenericAffine.mat'], 'invtransforms': ['/var/folders/6j/wrk7whwx57vgkytws9s8j42c0000gn/T/tmplr6t3kwx0GenericAffine.mat', '/var/folders/6j/wrk7whwx57vgkytws9s8j42c0000gn/T/tmplr6t3kwx1InverseWarp.nii.gz']}\n"
     ]
    },
    {
     "data": {
      "image/png": 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+V7vgZUslDOnyUV4o5z+gmuHEXv8O5er51Vr5nZMSPErEdSfmqWZX73eVHEydB01lBtk/+GQnxBNo7IR4Ao2dEE9gzL5DOCCURq/WUznPMM+EcsVpOt4unOmkr5w4vQwa90vbFNG5UelYJ+XV9o/6qx45UMYE1r7YQunGb5T+X1csBSW6f67TZvHXnf4v0s+NVU4h03In9u4xRo9T5JeILqe7nmZszhBd9cEiD86dqdphmCNP9Tsuzwaf7IR4Ao2dEE+gG78juHvvUPwrfqZUcSelZrvpl+X0lq8j5ri3u0Yu76beohsgu2vbyjLIALB4mvRj90k6XXXVXreHctcnnGl9f9Ypr6XOrk6b0lYqD6hxXPpVm7WuS568MF6kwxpbIK/Lr5F2fXL/q9qNuG12KI+/S6c6gb9m7phn8MlOiCfQ2AnxBBo7IZ7AmH0HcMSp/wnlXBv5PZ0pcWjOL3SMOn2JxKWdnPO76uIuKFkh15htdLBc6sTHeydliuyqyLTaF2LSr/3OzFe6K/aUwpfLp4vuc2jKnVv3i+wZ6JTVxHq3j5F2ptg5WKevb6QbSFbKe0mc2Fq1+x9nD7rxN/ZXOtyqV+P5DJ/shHgCjZ0QT+CWzfXGLYqgiy2euqJtKP8j706lm1UiibRC6Drs65JOhciErADriiWqXcdLJH81ZZz+vW7ruMy9jhcXNtlFr3rLfU1mqz28WKnQx5Fdd3yPYt2u9mXpf9VsvWKt+A5pXLlG+lRwgu4HzhVdeSdd0HJWYT/pr7OeryCmXfMWVhKQp1TomvJTW7/tHLmz65pv8Qpu2UyI59DYCfEEjsZvE5nqmZ2hjnrm/COUzR91vfYBuVIMwozU3patcWbXObusvrLLkardSVeIazroPd2TglJ5XXK4nN90vJ79lrhM+nXu89q1znHqx+EPIn7WTY+Cvxi/MJQv6/m40hW/JW534WTpU/Xe+vN4otcpoTzQ6iodAz+eE8pmonxWyWQkJPm59LekNPr8ckMsvxfJ8MlOiCfQ2AnxBBo7IZ7AmD0r2fZwc2NBHcvOaXVoKOec96TSJXOdmDoSe5o3JC797/+TBFhPo3NjZoy0q9UhsJ6R5sy0a/WlTmtNHyTbMv9upF6Z90zfa0K57TfzQ/ncXvq9XJEjK8ravKuXs1ln5qApdmrgr9HjFK3t2lAuR0ulMwmnsEVr+VOdfkFX1a7MtJF+WF0DXycSp8Jn+GQnxBNo7IR4At34+jL8glA8+A3tZl9gpDb8yq56kUmrpPjdLb7TEwrt8XJcaMUt7vPat6odFsqilqL99Wwyu1Z0sTdFTlbokKHrIJmV1zm2Wukqe0gfE2/I9Uce+bZq13Whs3Jlgu6iu5VVYne5d7xWL8jZLUeK5c9N9lA6WyufR2Ki9GPX3AWq3aafyvFlBboq34SlUkNvVRfXjY+GaM13Rt0W+GQnxBNo7IR4Ao2dEE9gzF5PdhsrRQ/HTz5b6WLl8hsaa6N/T1URhs5Khdkdu4Ty6qSk8/osjsTsOzuy3qYNdrZcf/17Eh+3rtb9WH+l5OgmJIcp3cZiJwVWIOmv1pHqEuZdJ6U2LzL1162+sbdzPrKdW68Pvg7lL/fronQvHnZMKJ/89euhHK/WcX/JeyL3HaGLZw4skj3o3sqYOgV0DN8843c+2QnxBBo7IZ5ANz7jSrZ0yDZJrR1XN95bu5W1yyR1ZXvo9NqrbY8L5VitnnXWH7NCuUN8jbRboH+TbZ6TRiuO1ClIyvG8hNy73UzdrtuC70I5r22V0nX4u/jaxpmhl0joNF9sqdOvSAF7291p69SGt+v05xGfJX+CbYfqVWnta2TVm62U15ki/V5mHSlF+mbX6Nl1x1U+EcpvYQ9HE6243/zhk50QT6CxE+IJdOOzEh2xFdd07l77h3L/adNVq193kO2Tzln+tNL1rJUdSBeY3kpXkpBFIR0mOi7t5IirPsA5bqdVpkJ0Jc75Hm0i5f/GyFc/6fwTlWruFbuF8qnrZfHLoOfm6mvMd0a3I9s6xZxS1cl8596R0XgUOfdNdFeq53NPDuXjLpe60kPMJ6pdv1slW9H/8BVKN2VfdyGM6+IvinTEXSjUPGfX8clOiCfQ2AnxBBo7IZ7AmD0r0a2DRoZSh5myauy39kbVqkW1xN6LO+opbn02yQyvwly92mx+Tt9Qrhom1+9RrAsyJDc4aaiF0DiX7OtsG2UPjcTsec52yI/plXktb5Qla4+8elsoxzbpPxfbxenHhsi4wkbn2Em3mXik3WQRz+/0vFJ9OEJm131VOyCUj3z1A9Vu9Y1S3f7NmoOVbmK1pNtM1+Ol70sehcZdLVeD5gif7IR4Ao2dEE+gG79NLA2lobWSbpsR31212pgrSa/ZVhe2qHLc585x7caftkx2f7V3OTXkV+sZeqZUXGGzSLvFlc6amfzezjU6RHZPdd3pbpFZbUZm6JlKeR7YvMg1nN2qkIi45185susV76ybGSdSsiX6+sWQ8KUmJmlQe5huV+1co19svtIdmP9xKD/7zpmhvKHvIN0RvIvmDp/shHgCjZ0QT6CxE+IJjNmzomNltDswFPdfK/u7fdFO7/V21/KbQzk2V/+eJiokwFx4iE55Pd75pFA++h6JIXc+bIPuR4ETs+4UUTlxtHX2WzbRmNrdi7mnVnVY46wI6+p8BnqHacDZHtr2i8Tz853CFjXOZ5AfSQHKQkKYXXQfa5xYvNr5U32q6CeqXXtIqnNuso/SVSadopufusUrIgX3VWGLO9Ec4ZOdEE+gsRPiCXTjsxLZ4vfnIv7v+NmhfHrhdarZhhMkTVTUStdrz3ta3MpeD+iZWh0vlhlkb+cfEso77/2xamdXiWwWRNxi1z13d2nWEQNsHyct11VfY1WpLEXr0ML5DMoideY2y+tikYISKjRw/sqSkeIVJua4+x/qZ0/RKGm7uVLc7pn5e6h2MyvLQ7lNTM827AqpKd/xWEmdzkc09eZM5eOqN0JIU4bGTogn0I3fBgptYSgvO7ttKHe9WJdYznnSqUE3SLutib7itsbX6dH+lg/KiPCmq5xhdl1WDWaF4zJHvkHrjnY7s9Vsr8hWU33l+LHWpyrdVQuuD+V1XfYM5dg67arHljuj8bobamacuzjHVEUaOrdeflKRUs1M9gvlvoVSk6+b/VS1W1CwSyj/BP9RurhTN699zUWhfOSfxup+XO2kBVCG5gif7IR4Ao2dEE+gsRPiCYzZs6ILTsbulvTMB7cMD+Uz93pJtcuZLB/r2uk6mi1t7xwfELmd1KvAmZf/O5RrI9sh57hbKxVrnXtsnSKTtlT347X2h4fyL667Qelq/yJjDnMrZI+qfmuWq3bJ1U5Rik2R54Zblt2ZeWcWR2YU9nWC+4SuRnnK11JkMjbJGd+IhNRDz5AViLUVembch+1lRt2jcdlmO+coXTe+9mo3nTcZzRE+2QnxBBo7IZ5AN/57s6Pc2VO6Bl1VlaR4Tl8ls90SkXUqdrC4tyWRhTCu67v+Ja1zZ5O1kp2PsCRy/Z5S1h32nEjSy6lPh24ifn2g3iH12VqpFR8bolOAOc6EwCFLJERZ22Ww7u9XTuotEe2H026ZtCuPuOAtH5fPoHP7SM2/HOmXO8svMVS3W76TpOw6btS6obVSx66q6NVQ/qi9fi/L1dTD5gmf7IR4Ao2dEE+gsRPiCcba6ETHHXgzYxruZvXGjdl1HGeGnh/KH044K5Q7YKVq1/1TZ1nayshbXubIUyOryKZIjFrrZJAKIuHkVx/KBm8Pm4uUrnOOTCsdkRwXynt+rAsxxt1UVmT/NesUx0j0lxi44nD9bGjpbP22YYAe/ilZ76z2e8VR3KPvVebE9qVDI/24QK6x/ABZwtd+ul6NaKY4BTh768/UDHSOnWKcj+x2nGp3UYt9nKPZ0DStVW/WWpPuPJ/shHgCjZ0QT2Dq7XuFCjKT49SkaxEX33dq7T6q3Yx9ZMpYOVoqnVvXfN8jtWuN34m4fIJ4Yl2H6FBgyNT3QnmPgV8q3cja10N50L9kb6jK23V6bZ2zxXJBHrSuWu6d4/yJdDpJt9vYUWahvdL7WKV7oVC2Wx4ySurpXfOY3uKpssz5E5wbKWzxlDyLOo6Tzzs2I/Jne5AjD9Gqj0r2lte1l9jo2qvv0g3xF0fORXOET3ZCPIHGTogn0I3PyiZ1VDNRho47WBmB32NCxB13vPolbfUuru2qZbQckyOlk6emT1bUFunFHfHOEibclrha6Yad841zc/l6C/Tmpiho7Rx8pXUbpyM9kfUhrYqkbnWPS+ZopZH6ekucqXwbb9MLUDrdLB1ZsECp0M7ZkalVb+e59AfdzjrFPTYX6efXAMwI5Y+NlAKveWS8voiqN8hdXAkhTRgaOyGeQGMnxBMYs2clGrvNCqW5ie6h3KaLTn/hJhF3LogUfFgkcXnNXB2zr9ogX0c3WWCH9XfolVx7xuR+h9ykt4RePEWu0cKZHdmmJrLN8aNS+fH6PW5Rui41MgZx6bhnRPG8TknNmijywTetULrnfi812sf8S4pGvH7yCNVuYQ/Zyil3of48WhY5xTecYYvKvvq9vFcoAxKVplDpWkBqyq9Jylbam67cX7XDrVPR3OGTnRBPoLET4gl047cJmZF23LcPhvK/+1+qWh24t+PWP6mv4P66mm7abe3c0TnoL+K0Er1C5N3HR4dyfLWeGdf9CHFxa28U39f8XX/V8bgcz1+j3eK4kQUoq1+Uou+tpuo0YgtnRuG8F5QK9w+WGWotRkld/Qcm/Uq1s7lOf6E/j4VOOf6C7ySkan2aTkUOf+ztUJ7RZjel62PlO/vazdFFN3GNFCppjvDJTogn0NgJ8QQaOyGewOIVWYmuiHPryI8MpTcqb1eteudKlYR2G3RqrGC+xKWJ/jqOzr/fOXjLkX8W6YYz0zN5nN4SunY3CUanOZUp95kyS7WLdZHf+b8UDle6MivF56/+7KlQLulSotrBqUUfv1mPHcApRpl8UvrYKvGFarbxyoGhvHSmjtm7Scl61PSV91VVrDeMc1cjxrpGingeKMeDd300lL/oFN10zt0/LppyZfEKQkgTgsZOiCcw9bZNuCujZGuiz2oHqFYD47KM7JNCPVPrkGKZqZX7gXbB7bMib1ovrmnLhyLRj+yijJoj9e/1uhq55j6fiuse66I9u+VdxSU/ulK71gs2SjWLluWylfFv97pWtdsjIe/lxN+/pXTxP0n/q06QPpZ3GqTaTXXCmnYm4n2eIe8752T5U63uoGfylcXkeF2NrsQxIXZUKH9xoVtk5GH4Bp/shHgCjZ0QT+BofFay1adzXMkev1Saja+KW5/bMlLPzB0Efl1/HHmviQu62Klm3G3nSMnp9uKq26Ogdf2cxSMtRU7upGeIbe4pmYUvanoqXetycf8rWst7WWi7q3YzYnuF8h4V7yvdqOemhPK6m8SlXwKNG0cWRHTucdtCeS+FB0XcfUmMINldh0bD9pTdcCcXuGGYDjuyF6/gaDwhpAlBYyfEE2jshHgCU29Zybads8OiJ9Rhq7sl9fbsIzcp3aFGYtu28QqlwwtOqsk5vXaFju1blzohWVutS+7ixObOVslW13TAyniHUN43+bXSvY4+oXziWhk82KdKz8I7tVKm8sU2RZ4bJ0mc3tYpnb/xSt3MDS5LI5PwSvdzDoZKy9q9IwU4u8oL1/fUkf+sB5zlg3BrxestpJprkUkXPtkJ8QQaOyGewNTbNuG68dm2CBK38poyPaOrT55s43pKzptKV5lTFMrt/iA16k2kMIRL8ieRhTBnixsfX+YsEPlUZ2MSBzjufgelQs034ibnLZb+228iIcNyuXfOzjoijB3j3M9ZJ7R4EjLS7SB9XHOv9GNDO4lDWr6j6/nnzJH3efFFf1a6R/KGOUcPOvK2uO1MvRFCmhA0dkI8gcZOiCcw9bZNZIrdMk+rvfO089XxZa+8Esr94rrWeqxG9lHOuUZSQ637V6t25g0JycwH+vc6LmE/7r1K6rUffcBrql3fb5x69jrsB5x94KxTo339TvpeJU5RjcUv63i+u7OP3ZLP5XzntjpttqlMrmk36utvbpMfyosSUsmi/2adAnxltAT7jw4/HJqnUDeS44KzAAAEYklEQVSaVlxeH/hkJ8QTaOyEeAJTb9uFbKvjSvXhQeL7XvT2OKU6HU+Hch+zKJTbvLtZtcvZXVJN9nh9edvZ+Yj3FnHz5XrV27wWMktuVnUXpduYkFhg93zZAnpscpRqd++M34Vy1U/1c2OTMznQJiXN1XaSjhzNn8Td3/Af/efRyvGsV58gdfGeSv5Utbv2mEtCueatl6FZ5MjuZ9C0V7Zlg6k3QjyHxk6IJ9CN3yFkc+tbOfLZStNmiXw804oPDOUO36pmmNe3byjvdu5CpbO1cg0jnjqW36jDieeT4v9vrtFeX89cyRKMuvN1UUzQ/TCHO2Wxn9DXiLUSl9n0kmfK6qeLVbv2T20M5dqpOtSo2V+yEOWnyWqa/r+co9qte+hR52gZNHWdKUc3nhDSTKCxE+IJNHZCPIEx+w6nrvE7APQW8SeHheLBLy9Xrf5dfkwoF90XqfiwlyNLWXfYSAEMnCDiyiNbKtUKI6m4QT+V+LjiCx0KzncygrGknobX71E5jjvPlLFH6VzhKWtfknZT9LPH7it9zrtumijGvAqNW8YyWpSirjBmJ4Q0E2jshHgC3fgdTjY3Pkq0cnpALHaCOi6eK+7+Pzvp9N1Rn8uqk8QiWXRicvXvetlJssjk1fLBSnfGM5Jjy5shRToSCe2qm7nOTq3DtO6Gm24L5ZPxfCjfjctVu5G1onuy4hilG3/SAaFsJzh7Y2EVNHV13ZuPq54NuvGEeA6NnRBPoLET4gmM2Xc42xKzu7hpueiUz11F7HWY0rR5V+L0g9rPCOWSuI5rX18pObpVfXVR+SfXy2q2U8olzWV+rqez5lwvK9jKBuQr3Rwr4wrTE9LfX8++Q7Xb8Ov1oWzfWADNh45c5sjNt1jk9oAxOyGeQ2MnxBPoxjcq9XXxs9WsL81wvmXk2G1XrjT5PU4J5c6fy5K7wws/V+06QXRrNunU22eV4sZPecApCH9HZMsrPOnIGyM6931mc939c9WzQTeeEM+hsRPiCTR2QjyBMXujUt+YPRvZ4vlMROPhTNeITufd2ZGjU1bdVJm7N1tklV69VqkxRs8GY3ZCPIfGTogn0I33lrqGENnSX3VNjWV6zdZeR3e9PtCNJ8RzaOyEeAJ3cSVpqOtuta4LHn1NpjBhWxaxkO0Jn+yEeAKNnRBPoLET4glMvRHSzGDqjRDPobET4gk0dkI8gcZOiCfQ2AnxBBo7IZ5AYyfEE2jshHgCjZ0QT6CxE+IJNHZCPIHGTogn0NgJ8QQaOyGeQGMnxBNo7IR4Ao2dEE+gsRPiCTR2QjyBxk6IJ9DYCfEEGjshnkBjJ8QTaOyEeAKNnRBPoLET4gkNuv0TIaTx4JOdEE+gsRPiCTR2QjyBxk6IJ9DYCfEEGjshnkBjJ8QTaOyEeAKNnRBPoLET4gk0dkI8gcZOiCfQ2AnxBBo7IZ5AYyfEE2jshHgCjZ0QT6CxE+IJNHZCPIHGTogn0NgJ8QQaOyGeQGMnxBP+P8RvlJBPZ+ArAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fixed = ants.image_read( ants.get_ants_data('r16') ).resample_image((64,64),1,0)\n",
    "moving = ants.image_read( ants.get_ants_data('r64') ).resample_image((64,64),1,0)\n",
    "fixed.plot(overlay=moving, title='Before Registration')\n",
    "mytx = ants.registration(fixed=fixed , moving=moving, type_of_transform='SyN' )\n",
    "print(mytx)\n",
    "warped_moving = mytx['warpedmovout']\n",
    "fixed.plot(overlay=warped_moving,\n",
    "           title='After Registration')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use the transforms output from registration and apply them directly to the image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD7CAYAAACscuKmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAE6RJREFUeJztnUmMlsUWhk87zzgwqKAydQMqTkArCAZjVFAJRk006tbEhQsTN7o2ceVajXsXxgiy0ZgQNWowgqJGxBYaBKS7aRwQEWftu7ix7lvv7e+7DaHB2+d5Vuenqr+v/t97bp1TZ6iOoaGhAICxz3HHegEAcHRA2QGSgLIDJAFlB0gCyg6QBJQdIAkoO0ASUHaAJJxwNF/W0dFBBg/AKDM0NNQx3L+zswMkAWUHSALKDpAElB0gCSg7QBJQdoAkoOwASUDZAZKAsgMkAWUHSALKDpAElB0gCSg7QBJQdoAkoOwASUDZAZKAsgMkAWUHSALKDpAElB0gCSg7QBJQdoAkoOwASUDZAZKAsgMkAWUHSALKDpAElB0gCSg7QBJQdoAkoOwASUDZAZKAsgMk4YRjvQD43xx33Mj/P/mvv/4q8mmnnVbkU045pZr322+/Ffnnn3+uxoaGhorc0dEx7L+3zfN1wD8DdnaAJKDsAEnAjD+GuOmruMmsnHDCf/6zqakeETF79uwid3d3F/miiy5qfH5vb281tmHDhiL//vvvRZ4+fXrjvEsuuaQa27hxY5H//PPPYWU4urCzAyQBZQdIAsoOkAR89n8QJ554YpE1dKU+ekTEo48+WmT3o0866aQiq19+1llnVfMmTJhQ5CuuuKIaO//884v866+/FnnevHnVvJkzZxZ51qxZ1VhnZ2eRP/300yJ/+eWX1byDBw8W2cN1uv62MwwYGezsAElA2QGS0HE0zaOOjg5sMcFDbxoee/zxx4t8xhlnVPM04+3MM8+sxi688MIia9acmu0Rdchuz5491djmzZuHXe/FF19cfdYsvIkTJ1Zj33zzTZHXrl1b5L6+vsZnaLguImLr1q1FVhMfk76doaGhYWO67OwASUDZAZKAsgMkgdDbUUYr2DTEFRHxyCOPFFl973POOaeapymnP/zwQzU2ZcqUIquf7uG7P/74Y9g1+WcN5WnqbETtp5999tnVmPrm+l2uuuqqap6GG6dOnVqNvfLKK0Xu6emJJvDhRwY7O0ASUHaAJGDGjzIeXlNzevny5dXYjBkziqwmuJvZ5513XpEPHDhQjZ188slFVhPZUTPe16hZbRqiO/7446t56l54CPDUU08tsroWniWnrkFXV1c1tmLFiiKrO6EZef5MTPpm2NkBkoCyAyQBM34UaGtKccsttxT5mmuuqcbU7NbCFTfVFT3pjqjNbjXVv/vuu2re3r17i+w96PSzmsg//fRTNU/X5Vl+erL+7bffDitHROzcubPImk0XUUcr9BTfn9Hf3z/seiMw6xV2doAkoOwASUDZAZKAzz4KtPnsixYtKrI3g9CwmYayPINO/WMPr2l4TH3q3bt3V/MGBgaKrGEtZ//+/Y1j6mO7b3zuuecWWX+PH3/8sZqnITpvsKFnDkuXLm1cx+rVq4vsGYXwH9jZAZKAsgMkATN+lNGsuIi6YMSLR7TZhIbQxo0bV81TU91DTWoyq1swadKkxnft27evGtMQm7oQ33//fTVPXQEPAaproOa596/XTEF3J9RF0TV6Qc6WLVuKvH79+mpMf5/sV1KxswMkAWUHSALKDpAEfPZRQH3xhx9+uBrTyq7BwcFq7PTTTy+yNnf0qre2q5L1sz7P72LTkJqvQxtQakqvhtMi6uo797ebGmDo30TUfrT2qI+om1bqer15pqbVZvfL22BnB0gCyg6QBMz4UWDZsmVF1muTI+qMsV9++aUa089qjnqoSUNvbWa8yp5pp6E9D5vpdVAaKvN5betoWpM3wFCXxE1w/axugYcs586dW+RPPvmkGvPrpjLDzg6QBJQdIAkoO0AS8NlHgcsvv7zI7stqOMxTaTWFVXvDewcXbVrpvrhWijX5vBG1zz5nzpxqTP10XZOH1/S76Xv/17ub5nlve03V1Wf43XR6luDdf7Zv39747mywswMkAWUHSAJm/BHAzVQ1rb2pQ29vb5F37NhRjam5riayN3OcNWtWkdsy0rTJpGenaTacN43Qxhn6PA8V6tXOau5H1Bl7Gm7zyjk1yf35mjWnz/Dfe+bMmUX+6quvqjH9HbM3n2RnB0gCyg6QBMz4I8AFF1xQfdYTd2/WoJ+9/7manGrS+4mymvV+7ZKa3dqP7euvv67maU86vY01ImL8+PFFVldDC1MiIrZt21bkyy67rPEZul7vQaeugBe46LVR3sBD0SYabTfSZi+SYWcHSALKDpAElB0gCfjsh4D61OoLamPHiNq39WyvtvCPhtG2bt1aZPfLtVmDZ+hp5p2GAD18p+tat25dNab30T3zzDNF1uqyiLo6zhtbNK3Js+T0t/PqPv1u+gz9bSLqsKJn+en5SVsP/AywswMkAWUHSAJm/GFy/fXXF/mGG26oxrSwxK9DHqn5rxlj3j9O/+7gwYPVmJru2uTBQ1J6NZRnv2nfeA3faV+8iNo18IYSug59nq9D/87N7KYsQi8gUpdEXYuIiIceeqjITz/9dGSGnR0gCSg7QBJQdoAk4LMfJkuWLCny3XffXY2pv+1NHdQP9Uo09Vl1zH17TaX1cJU2tFQ/131lTcH18KCuue2MQcNt+l5fo54r+Do0jOgVcZs2bSryggULGp/hlX+KVvQ1hU4j6tDeWIWdHSAJKDtAEjDjD4GmPukeXtMsLm/IsHPnziK7Kak91zQc5qExzcJrMz81W83fpVl53stdK/PaMv70e/oztMJMn+Hr0Ay3tuy3tuo1reDTUGFEHaZrysjLAjs7QBJQdoAkYMa30HZ76qpVq4rsDRmuvPLKImu/uIj2jDE97VaT3otY1G1ouxpK1+tRAW24cccdd1RjW7ZsKfJ1111XZO31FlG7FyNtF912TZSb4Box0DFvtqG/sZ/oa086Xa+7Xhn607GzAyQBZQdIAsoOkAR89hbcj1Nf8bbbbivyvHnzqnkaQvKqNA15eahJfXgNDXnTSvXLPSyn4TbNYnN/WL+bX8Ws/uztt98+7Hv9+T6mn71hhaLnEfPnz6/GtMe+NufU67Ui6muZNQwXUTfa7OrqKrJm50XUv/dY9d/Z2QGSgLIDJAEz/hDQbDgt4NBrliJq89bDZmoi6k2qEXVoa/LkyUV2E1nDaF6c0mTGe4hO8YIcDY81hfIiatPXQ2r63Zr64fuYhwfVzWm61srX5U00NMS4ePHiIu/ataua588ci7CzAyQBZQdIAsoOkAR89hbcD9UQmDZf9GuCFy1aVGT3yzWtdO/evdVYT09PkTVkd/XVV1fz1H91P1f9aB3z76Jjnjqq5wB6TuFnB4o/Q0OCbdcm63fxZ+iYfi+/+04bZ3i6rD5DQ5u+jgxXO7OzAyQBZQdIAmZ8C94kYfbs2UXW/uR+XbH2f3czW0N206ZNq8a013p/f3+RPQtPw2ueQafvU3PUTVPt5e4NNjQ81nSdVER7QwkN5+m73RXQMa30i6hNcp23b9++ap6a5x6K1IzFSZMmFVl70/m7xirs7ABJQNkBkoAZfwioSasFF3rdU0TEhAkTiqzmckR94uwFIlOmTCnyjh07iuxZZ2pOt51u69/5SbeuS5tVRER8+OGHRX7wwQeHfW/EfxfyKHq63dYLTwtcBgYGqjE11zUT0U1wNeP9qixdh0ZG+vr6qnn62V2vsQI7O0ASUHaAJKDsAEnAZ2/B/cs33nijyCtXriyy+4na5MKvRVK/3xtVqg980003FVnDRxF1BZv3P2/KrvOqN+1fv2bNmmrss88+K7I2o3SfXT+3NefUMffzNazoWX6dnZ1F1mo2D98NDg42rkPHPv/88yLruUpE/Rt7GG6sZNSxswMkAWUHSAJmfAtuvul1RNpowk1HNc/besSNGzeu8d36d262asabF9PoXJ2n5mxE3bfNGz5oduCrr75a5Hvvvbeap7+Hm+C6jrZrojSj0G9j1d9fM/I8g06/p7sJetOsXgXlv5u7KGMRdnaAJKDsAElA2QGSgM/egvuXU6dOLbJWtnlKrPqe7kPqZ29G6Z//xsNmGzduLLKGySLqUJ+GBL03/H333VdkP3PQcJ5+N2/KqGcTfiagZxMa5tLKs4j63MKr+/bs2VNk9dM9XVabinhasK5RfXb/b/b8888XeayE2hx2doAkoOwAScCMPwTUvFMz1cM4WsnlFVRqtvrVw9rXTk1VN29ffvnlIrtZrK6Ahge1f3pEbbq72do05s0lNOT1xRdfVGPaJ07Da/fcc081r+0qK/19NITmff0OHDhQZL/mSq9/0vDgSy+9VM3T0J6HEceKWc/ODpAElB0gCZjxLXiRiTas0FNvv2VVzUo/9dXTeM+g04w0xfu7qbnb3d1djV177bVF1n5sXnSj7oWbxW5O/41HC/Tv/IRcTWF9t7eBnj59epE9cqHFKdqIQxt7RNTuhP83U9dDT/fffvvtap7+xmPFbHfY2QGSgLIDJAFlB0gCPnsL7rup76khHvdlNXPN/W31o9031pCX+qjbtm2r5qkf6ldD6VrarofeunVrkd99991qTH3lhQsXFnn8+PGN83wdvb29Rd6wYUORvdHjY489VmT32f23+5u2TDsPdWo1m4bXli1bVs3TcOZYhZ0dIAkoO0ASMOMPAS2yePPNN4t84403VvO0GYSbvooXuKgJquEkzQKLiHjggQeK7E0X1L3QPnNeCKN4X3pdl4bUvJ+eugma4RZRm8lqnmtPen+Go1mKmmmn13BF1O6PN7bQ9asb5u8dq+E2hZ0dIAkoO0ASUHaAJOCzHwIaunnxxReLvGTJkmqepnNqWCiiDoF5Kq3e9aaptJdeemk1T/1QrbCLqFN1tRrPrzLWtFKvnFMfXpt0eIMKPWPw3vlaZbdgwYIib968uZrn61L07EDPS/ysQ9/t1X16hrF27dph5SywswMkAWUHSAJm/CGgGV0aDnNTWnvQedhs8uTJRfZ+7Rq+0lCTm9lq/vu7NYSkboG6IBF1FpqH5dRkVjPbTV8NK3roSisBNVT25JNPVvM0nOcZc/rd9Hf079J05VVExK5du4r8wQcfFFndnSywswMkAWUHSAJm/GGipuNzzz1XjT377LNFbstca7vhte2GVDWt/fRZTXxv5KBodpo3ttBiGi1o8euZ9KTe16guibor/p0V7/2mveu0HbUXwmjWnLeS1kxHbcGdEXZ2gCSg7ABJQNkBkoDPfgTYtGlT9VnDS/fff381pj68X6fUFMrya6jUF/dQU1PYzMNaOs9DgOqLN/nNEXVfej0DiKhDZfpu7Y3vtK2xrTe8huK0WjCiPnPQUJ6/i6o3ABgzoOwAScCMPwK4Kf3aa68Vefny5Y1z9VbYiIiPPvpo2DFvDKFmvYey9u/fP+waPQtP+9958wrt5a7P96IVDQ96cYq6KB6ya8ILg9QE1+e726HPX716dTX2/vvvF1ndnwxmu8PODpAElB0gCSg7QBLw2Q+Tkfp83o98xYoVRfb70TTlVKu1PAVUfVbt3R5R+6VapebNJdzvVUbaNKKrq6vIfp2zptxqww5/hp4/eIhRzzc0RdbPMLSJ5Zo1a6qxgYGBgH/Dzg6QBJQdIAmY8aOAmvjvvPNONaam6cqVK6uxzs7OYZ/R1pBhzpw51Zia9bfeeuuwfxNRZ5N5uK4pzKWhsIjaVPfrppuaUvT09FTztMmFuzWaUajuyXvvvVfNe+GFF4r88ccfV2P+22WGnR0gCSg7QBIw40cZNyPXrVtXZL2eKSLirrvuKvKdd95ZZD+Nb2vW0ISfgvf39xfZi0LUPJ87d26RFy9eXM1rytbz92mhimfJaYGLr0ObWWikYtWqVdU8vSXWC2EyZso1wc4OkASUHSAJKDtAEjqOpk/T0dGBAyV4xphmmumVSQsXLqzmaUhNQ1cRdaaZ+t5+tbNWpXlve31Gd3d3kb3CTp/p/zvSMJ363lrZF1FX2OmVVxF1k4qnnnqqyNpEMqL2093vz8jQ0FDHcP/Ozg6QBJQdIAmE3o4h3tddzee33nqryH7zqYa8li5dWo3NmDGjyBqW85CXZtB5scj8+fOLrCE0D9+pWe994bQIR9fk4UYNqW3fvr3xGevXry+yN9vAdB8Z7OwASUDZAZKAsgMkgdDbPxQNy/l/I60w0wYSEXVKqzZi9NDb4OBgkV9//fVq7IknnijytGnTiqx94iMidu/eXWS/f03PFTTMp753RF0F52cT+neadkwKbDuE3gCSg7IDJAEz/v8Qv9pY8WuYmv5dP3sIUJto3HzzzUWeOHFiNU8z/vwZWpmn12O5Gd/X19f4DP2emO4jBzMeIDkoO0ASUHaAJOCzjzHa/Pkm/H8DTc/Q1NaI+r44T1nV1Fr1xf3ZpLoeefDZAZKDsgMkATMeWmkLfx1OaMzNeEJqRx7MeIDkoOwASaB5BbTSZmYfjgmO2X7sYGcHSALKDpAElB0gCSg7QBJQdoAkoOwASUDZAZKAsgMkAWUHSALKDpAElB0gCSg7QBJQdoAkoOwASUDZAZKAsgMkAWUHSALKDpAElB0gCSg7QBJQdoAkoOwASUDZAZKAsgMkAWUHSALKDpAElB0gCSg7QBJQdoAkoOwASejgCl2AHLCzAyQBZQdIAsoOkASUHSAJKDtAElB2gCSg7ABJQNkBkoCyAyQBZQdIAsoOkASUHSAJKDtAElB2gCSg7ABJQNkBkoCyAyQBZQdIAsoOkASUHSAJKDtAElB2gCSg7ABJ+Be5m80SVP8wCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mywarpedimage = ants.apply_transforms(fixed=fixed, moving=moving,\n",
    "                                      transformlist=mytx['fwdtransforms'])\n",
    "\n",
    "mywarpedimage.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other utilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "N3 and N4 bias correction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = ants.image_read( ants.get_ants_data('r16') )\n",
    "image_n4 = ants.n4_bias_field_correction(image)\n",
    "ants.plot( image_n4 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Move to & from nibabel images with `ants.to_nibabel()` and `ants.from_nibabel()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
